## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))
### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(read.csv("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by="state")Lab11
Grab lab file using command line:
# Step 1
cd ~/Documents
mkdir lab11
cd lab11
# Step 2
wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/11-lab.RmdAnd remember to set eval=TRUE
Learning Goals
- Read in and process the COVID dataset from the New York Times GitHub repository
- Create interactive graphs of different types using
plot_ly()andggplotly()functions - Customize the hoverinfo and other plot features
- Create a Choropleth map using
plot_geo()
Lab Description
We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.
The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this
Steps
I. Reading and processing the New York Times (NYT) state-level COVID-19 data
1. Read in the data
- Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv”
- Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv””
- Merge datasets
2. Look at the data
- Inspect the dimensions,
head, andtailof the data - Inspect the structure of each variables. Are they in the correct format?
print(dim(cv_states))[1] 58094 9
print(head(cv_states)) state date fips cases deaths geo_id population pop_density abb
1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL
2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL
3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL
4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL
5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL
6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL
print(tail(cv_states)) state date fips cases deaths geo_id population pop_density abb
58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY
58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY
58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY
58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY
58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY
58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WY
print(str(cv_states))'data.frame': 58094 obs. of 9 variables:
$ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
$ date : chr "2023-01-04" "2020-04-25" "2023-02-26" "2022-12-03" ...
$ fips : int 1 1 1 1 1 1 1 1 1 1 ...
$ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ...
$ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ...
$ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
$ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
$ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
$ abb : chr "AL" "AL" "AL" "AL" ...
NULL
For the most part the variables look good. however, the date variable is in chr format. state is also not factored
3. Format the data
- Make date into a date variable
- Make state into a factor variable
- Order the data first by state, second by date
- Confirm the variables are now correctly formatted
- Inspect the range values for each variable. What is the date range? The range of cases and deaths?
# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)
### FINISH THE CODE HERE
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# Confirm the variables are now correctly formatted
str(cv_states)'data.frame': 58094 obs. of 9 variables:
$ state : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
$ date : Date, format: "2020-03-13" "2020-03-14" ...
$ fips : int 1 1 1 1 1 1 1 1 1 1 ...
$ cases : int 6 12 23 29 39 51 78 106 131 157 ...
$ deaths : int 0 0 0 0 0 0 0 0 0 0 ...
$ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
$ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
$ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
$ abb : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states) state date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
tail(cv_states) state date fips cases deaths geo_id population pop_density abb
57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY
57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY
57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY
57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY
58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY
57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states) state date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states) state date fips cases
Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1
Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125
California : 1154 Median :2021-09-11 Median :29.00 Median : 418120
Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941
Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318
Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158
(Other) :51184
deaths geo_id population pop_density
Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
Median : 5901 Median :29.00 Median : 4468402 Median : 107.860
Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031
3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120
NA's :1106
abb
WA : 1158
IL : 1155
CA : 1154
AZ : 1153
MA : 1147
WI : 1143
(Other):51184
min(cv_states$date)[1] "2020-01-21"
max(cv_states$date)[1] "2023-03-23"
Date min: 2020-01-21; Date max: 2023-03-23 cases range= 12169157; death range= 104277
4. Add new_cases and new_deaths and correct outliers
Add variables for new cases,
new_cases, and new deaths,new_deaths:- Hint: You can set
new_casesequal to the difference between cases on date i and date i-1, starting on date i=2
- Hint: You can set
Filter to dates after June 1, 2021
Use
plotlyfor EDA: See if there are outliers or values that don’t make sense fornew_casesandnew_deaths. Which states and which dates have strange values?Correct outliers: Set negative values for
new_casesornew_deathsto 0Recalculate
casesanddeathsas cumulative sum of updatednew_casesandnew_deathsGet the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively
# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
cv_subset = cv_subset[order(cv_subset$date),]
# add starting level for new cases and deaths
cv_subset$new_cases = cv_subset$cases[1]
cv_subset$new_deaths = cv_subset$deaths[1]
### FINISH THE CODE HERE
for (j in 2:nrow(cv_subset)) {
cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j - 1]
cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j - 1]
}
# include in main dataset
cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2021-06-01")
### FINISH THE CODE HERE
# Inspect outliers in new_cases using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)p1<-NULL # to clear from workspace
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
# add starting level for new cases and deaths
cv_subset$cases = cv_subset$cases[1]
cv_subset$deaths = cv_subset$deaths[1]
### FINISH CODE HERE
for (j in 2:nrow(cv_subset)) {
cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j - 1]
cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j - 1]
}
# include in main dataset
cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}
# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)
# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)p3<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p3)Florida has an outlier of new cases (-40527) in 2021-06-04.Texas also has a strange high of 87445 cases on 2023-03-15.Massachusetts has an outlier of new deaths of -3770 on 2022-03-14. New York has a spike of new deaths of 3237 on 2022-11-11 After the resetting to 0, new deaths has a spike in new york 553 on 2022-11-11. Florida also has a spike of 445 on 2021-09-20. There is a spike of new cases in California on 2022-01-15 at 119536
5. Add additional variables
Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (
numeric). You can use the following variable names:per100k= cases per 100,000 populationnewper100k= new cases per 100,000deathsper100k= deaths per 100,000newdeathsper100k= new deaths per 100,000
Add a “naive CFR” variable representing
deaths / caseson each date for each stateCreate a dataframe representing values on the most recent date,
cv_states_today, as done in lecture
### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))II. Scatterplots
6. Explore scatterplots using plot_ly()
- Create a scatterplot using
plot_ly()representingpop_densityvs. various variables (e.g.cases,per100k,deaths,deathsper100k) for each state on most recent date (cv_states_today)- Color points by state and size points by state population
- Use hover to identify any outliers.
- Remove those outliers and replot.
- Choose one plot. For this plot:
- Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
- Add layout information to title the chart and the axes
- Enable
hovermode = "compare"
### FINISH CODE HERE
# pop_density vs. cases
cv_states_today %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))# filter out "District of Columbia"
cv_states_today_filter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))# pop_density vs. deathsper100k
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))# Adding hoverinfo
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
hoverinfo = 'text',
text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") ,
paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) %>%
layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
hovermode = "compare")Based on the scatterplots California and Texas had the most cases with DC presenting as an outlier. When DC is filtered out California has the most cases with lower pop density. New Jersey has high pop density but relatively low cases. When comparing pop density and deaths per 100k, Arizona has high number of deaths per 100k and low pop density. Hawaii has low pop density and low deaths per 100k. New Jersey has high deaths per 100k and high pop denstiy ### 7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()
- For
pop_densityvs.newdeathsper100kcreate a chart with the same variables usinggglot_ly() - Explore the pattern between \(x\) and \(y\) using
geom_smooth()- Explain what you see. Do you think
pop_densityis a correlate ofnewdeathsper100k?
- Explain what you see. Do you think
### FINISH CODE HERE
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth(method = 'lm', se = TRUE, color = "blue") +
labs(title = "Population Density vs. New Deaths per 100k",
x = "Population Density",
y = "New Deaths per 100k") +
theme_minimal()
ggplotly(p)population density and new deaths per 100k appear to be positively correlated
8. Multiple line chart
- Create a line chart of the
naive_CFRfor all states over time usingplot_ly()- Use the zoom and pan tools to inspect the
naive_CFRfor the states that had an increase in September. How have they changed over time?
- Use the zoom and pan tools to inspect the
- Create one more line chart, for Florida only, which shows
new_casesandnew_deathstogether in one plot. Hint: useadd_layer()- Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?
### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>%
filter(state=="Florida") %>%
plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>%
add_trace(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines") Leading up to september there was a drop in naive CFR. In september it started to increase. Following January 2022, there was a decrease in naive CFR that then increased again in April 2022. By Jan 2023 it has slightly decreased and plataeud. The peak date of deaths for Florida was Sept 20, 2021 with 445 deaths. The peak date of new cases was Jan 10 2022 with 84.669K cases. The time delay between sept 20 2021 and Jan 10 2022 is about 112 days
9. Heatmaps
Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix. Which states stand out? - Repeat with newper100k variable. Now which states stand out? - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks
### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>as.Date("2021-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)Graph 1: California, Texas, NY, and Florida stand out Graph 2: For the most part all states slightly stand out. However, Rhode Island really stands out and Idaho doesnt really stand out Graph 3: It is now easier to see which states and time frames stand out. In Aug 2021, Louisiana and Mississippi stand out, Sept 2021 South Carolina and Tennessee stand out, and in Oct 2021 Alaska stands out ### 10. Map
- Create a map to visualize the
naive_CFRby state on October 15, 2021 - Compare with a map visualizing the
naive_CFRby state on most recent date - Plot the two maps together using
subplot(). Make sure the shading is for the same range of values (google is your friend for this) - Describe the difference in the pattern of the CFR.
### For specified date
pick.date = "2021-10-15"
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 125
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k, text = ~hover, locations = ~state,
color = ~newper100k, colors = 'Purples'
)
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
geo = set_map_details
)
fig_pick.date <- fig
#############
### Map for today's date
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k, text = ~hover, locations = ~state,
color = ~newper100k, colors = 'Purples'
)
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
geo = set_map_details
)
fig_Today <- fig
### Plot together
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)When comparing oct 15th 2021 to today (nov 13), there are many more cases in 2021 vs now. Today there is little to no purple indicating little cases. In 2021 there is more purple. Alaska had a lot of cases with Montana, Idaho, and Wyoming having many cases as well